A Novel Optical Fiber Reflectometry Technique with High Spatial Resolution and Long Distance

Spatial resolution is an important parameter in distributed optical fiber Raman temperature sensor system (DOFRTS). In this paper, a 10 kilometers long DOFRTS with spatial resolution of about 6 meters is constructed. The spatial resolution is limited by electrical bandwidth of the photodetector circuit and the data acquisition part. The abrupt temperature changes along the fiber axis are treated as temporal pulse signals, and a linear amplitude coefficient modification algorithm is used to improve the spatial resolution. The experimental results show that the temperature amplitudes from 3 meters region to 6 meters can be modified accurately. Therefore, a DOFRTS of high spatial resolution but low system cost could be successfully constructed.

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High spatial resolution distributed optical fiber temperature measurement system for ice cover profile

Journal of Applied Optics ◽

10.5768/jao202142.0508003 ◽

2021 ◽

Vol 42 (5) ◽

pp. 941-948

Author(s):

WANG Dingrui ◽

◽

DENG Xiao ◽

ZHANG Jun ◽

...

Keyword(s):

Optical Fiber ◽

Temperature Measurement ◽

Spatial Resolution ◽

Measurement System ◽

High Spatial Resolution ◽

Ice Cover ◽

Temperature Measurement System ◽

Distributed Optical Fiber

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Real-Time Stress Concentration Monitoring of Aircraft Structure During Flights Using Optical Fiber Distributed Sensor with High Spatial Resolution

ICAF 2019 – Structural Integrity in the Age of Additive Manufacturing - Lecture Notes in Mechanical Engineering ◽

10.1007/978-3-030-21503-3_85 ◽

2019 ◽

pp. 1082-1090

Author(s):

Daichi Wada ◽

Hirotaka Igawa ◽

Masato Tamayama ◽

Tokio Kasai ◽

Hitoshi Arizono ◽

...

Keyword(s):

Stress Concentration ◽

Optical Fiber ◽

Real Time ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Aircraft Structure ◽

Distributed Sensor ◽

Time Stress

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Robust Building Extraction for High Spatial Resolution Remote Sensing Images with Self-Attention Network

Sensors ◽

10.3390/s20247241 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7241

Author(s):

Dengji Zhou ◽

Guizhou Wang ◽

Guojin He ◽

Tengfei Long ◽

Ranyu Yin ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Image ◽

Context Information ◽

Building Extraction ◽

Remote Sensing Images ◽

Global Features ◽

Long Distance ◽

Attention Network

Building extraction from high spatial resolution remote sensing images is a hot spot in the field of remote sensing applications and computer vision. This paper presents a semantic segmentation model, which is a supervised method, named Pyramid Self-Attention Network (PISANet). Its structure is simple, because it contains only two parts: one is the backbone of the network, which is used to learn the local features (short distance context information around the pixel) of buildings from the image; the other part is the pyramid self-attention module, which is used to obtain the global features (long distance context information with other pixels in the image) and the comprehensive features (includes color, texture, geometric and high-level semantic feature) of the building. The network is an end-to-end approach. In the training stage, the input is the remote sensing image and corresponding label, and the output is probability map (the probability that each pixel is or is not building). In the prediction stage, the input is the remote sensing image, and the output is the extraction result of the building. The complexity of the network structure was reduced so that it is easy to implement. The proposed PISANet was tested on two datasets. The result shows that the overall accuracy reached 94.50 and 96.15%, the intersection-over-union reached 77.45 and 87.97%, and F1 index reached 87.27 and 93.55%, respectively. In experiments on different datasets, PISANet obtained high overall accuracy, low error rate and improved integrity of individual buildings.

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